Lab part 2

Preformatted Flagella Regeneration Data

Here are two data sets for deflagellated cultures, followed by two partially analyzed data sets from non-deflagellated controls. From most browsers you should be able to paste your class' data into a spreadsheet directly from
the browser window.

The data sets were generated by the Tuesday and Wednesday sections of Bios 211 in spring 2006. We took replicate samples at each time interval, and each
line of data represents information from one culture reported for one time
interval by one individual. I suggest that you plot the means ± errors for each individual
sample rather than pool all of the data for each time interval.

For both the Tuesday and Wednesday sections the results for deflagellated cultures came out great.
There was experimental error, of course, but the
trends are clear. These data are not "fudged." A
couple of data sets that were obviously collected
incorrectly were removed. Zeroes were averaged
into the raw data up to and including 30 minutes, then only
cells with flagella were averaged. The rationale is that by 40 minutes cells without
flagella were not regenerating them at all and thus were no longer part of the experiement.
Everything else is presented exactly as reported.

For each data set look at the raw data,including data for
the control groups. Are they consistent? Do you
think the class did an adequate job of ensuring
that cultures were scored consistently? Don't answer these questions in the search paper, but do consider them as you draw conclusions.

Nondeflagellated cultures

Below are two sets of data, collected from different cultures by different individuals. You may describe these findings in your results section as representing data from two identical experiments. You will need to report means ± standard deviations for each data set. For set 1 the means (avg) and standard deviations (std dev)are computed for you, and "Student's" t test was used to compare average length at 120' with starting length (0') using "Student's" t test. You are to conduct the calculations for set #2.

You can conduct a t test on an Excel spreadsheet using the Insert/Function... menu. First, select a blank cell. From the pop-up menu choose Statistical and then TTEST from the list. In the dialog box that comes up select the arrays (use the mouse to select the cells containing each data set). Select #tails. You will need a two-tailed test because you are asking simply if there is a significant difference, and the flagella could have lengthened or shortened. Select the type of ttest that is appropriate for these data and click OK. To help you make the choice go to Writing/analytical resources (course web site), select Statistical Methods, and examine the pages on "Student's" t test.

If you are unsure what test to use after perusing these materials, then you might try some of the sample problems (link will be above the title of each page). If you intend to conduct an unpaired test, then choose the two-sample test type 2. The probability that the null hypothesis is correct (p value) will appear in the cell. This is not the t statistic. Papers of this kind usually don't require reporting a t statistic, just the p value.

When you report an outcome it must be completely clear what was compared to what. Remember that p < 0.05 suggests a significant difference, while a large p value suggests no difference. This is because the p value represents the probability that the observed difference in means was due to random (experimental) error. We usually do not report an exact p value. If there is no significant difference you might write, "p > 0.05" or "no significant difference," or perhaps "n.s.," with explanation of the abbreviation in a footnote. We usually represent a significant difference using the "<" symbol. If 0.02 < p < 0.05, we write "p < 0.05." If 0.01 < p < 0.02, we write, "p < 0.02." If 0.005 < p < 0.01, we write, "p < 0.01." We continue in the same vein for 0.002 < p < 0.005 and 0.001 < p < 0.005. If p < 0.001 then that's what we write. We don't usually go lower.

The lower the p value the greater is our confidence that the difference is real (not due to random error). These cut-offs, by the way, are left with us from the days before personal computers, when we had to read p values from tables.

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Created by David R. Caprette (caprette@rice.edu), Rice University Dates